How does the model supervise the process of Semantic Decoder and Boundary Decoder?

[jahoyan]

Hello, I have a question about your model, that is, how does the model supervise the process of Semantic Decoder and Boundary Decoder?How are parameters updated in the backbone,Semantic Decoder and Boundary Decoder?

[jahoyan]

Hello, I have another question that you used OTP to match the gt_point and the sampling point.But in your config code you use HungarianAssigner to match the gt_point and the sampling point.So I want to know what's the difference between the two methods

[LiWentomng]

Hello, I have another question that you used OTP to match the gt_point and the sampling point.But in your config code you use HungarianAssigner to match the gt_point and the sampling point.So I want to know what's the difference between the two methods

@jahoyan
OT is used for generating pseudo mask. Based on the pseudo mask, the HungarianAssigner is used to assign pos/neg samples same to original fully supervised method.

[LiWentomng]

Hello, I have a question about your model, that is, how does the model supervise the process of Semantic Decoder and Boundary Decoder?How are parameters updated in the backbone,Semantic Decoder and Boundary Decoder?

@jahoyan
The parameters in Semantic Decoder and Boundary Decoder are driven by the weakly supervised loss, inlcuding semantic loss and boundary loss. Please see the Sec.3.4.1 in the paper.